// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen3@codeplay.com>
// Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.


#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC                cxx11_tensor_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

void test_sycl_cpu(const Eigen::SyclDevice& sycl_device)
{

    int              sizeDim1    = 100;
    int              sizeDim2    = 100;
    int              sizeDim3    = 100;
    array<int, 3>    tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
    Tensor<float, 3> in1(tensorRange);
    Tensor<float, 3> in2(tensorRange);
    Tensor<float, 3> in3(tensorRange);
    Tensor<float, 3> out(tensorRange);

    in2 = in2.random();
    in3 = in3.random();

    float* gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(float)));
    float* gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(float)));
    float* gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.dimensions().TotalSize() * sizeof(float)));
    float* gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(float)));

    TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
    TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
    TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange);
    TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);

    /// a=1.2f
    gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
    sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data, (in1.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(in1(i, j, k), 1.2f);
            }
        }
    }
    printf("a=1.2f Test passed\n");

    /// a=b*1.2f
    gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * 1.2f);
            }
        }
    }
    printf("a=b*1.2f Test Passed\n");

    /// c=a*b
    sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.dimensions().TotalSize()) * sizeof(float));
    gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * in2(i, j, k));
            }
        }
    }
    printf("c=a*b Test Passed\n");

    /// c=a+b
    gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) + in2(i, j, k));
            }
        }
    }
    printf("c=a+b Test Passed\n");

    /// c=a*a
    gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * in1(i, j, k));
            }
        }
    }
    printf("c= a*a Test Passed\n");

    // a*3.14f + b*2.7f
    gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), in1(i, j, k) * 3.14f + in2(i, j, k) * 2.7f);
            }
        }
    }
    printf("a*3.14f + b*2.7f Test Passed\n");

    /// d= (a>0.5? b:c)
    sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(), (in3.dimensions().TotalSize()) * sizeof(float));
    gpu_out.device(sycl_device) = (gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
    sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(float));
    for ( int i = 0; i < sizeDim1; ++i ) {
        for ( int j = 0; j < sizeDim2; ++j ) {
            for ( int k = 0; k < sizeDim3; ++k ) {
                VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) > 0.5f) ? in2(i, j, k) : in3(i, j, k));
            }
        }
    }
    printf("d= (a>0.5? b:c) Test Passed\n");
    sycl_device.deallocate(gpu_in1_data);
    sycl_device.deallocate(gpu_in2_data);
    sycl_device.deallocate(gpu_in3_data);
    sycl_device.deallocate(gpu_out_data);
}
void test_cxx11_tensor_sycl()
{
    cl::sycl::gpu_selector s;
    Eigen::SyclDevice      sycl_device(s);
    CALL_SUBTEST(test_sycl_cpu(sycl_device));
}
